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Urban modelling and urban understanding have been two active research topics for years in the communities of computer graphics and computer vision. Their applications benefit a wide variety of areas such as entertainment production, urban planning and city management. Although conventional approaches have gained considerable successes on resolving relevant problems, there still exist critical gaps about how to deal with high-dimensional solution spaces due to the complexity of a city. The rise of machine learning, however, provides a new perspective for addressing such concerns, and a number of works have shown the potential of machine learning methods for resolving problems of urban modelling and understanding. In a context of urban modelling and understanding, this talk reviews the mainstream approaches and focuses on the application of machine learning. A brief introduction to the EPICentre’s latest achievements on visualisation is also included.
Dr Tian (Frank) Feng is currently serving as a postdoctoral fellow at the UNSW Art & Design, Expanded Perception and Interaction Centre (EPICentre). He received his PhD degree in Information Systems Technology and Design from the Singapore University of Technology and Design in 2017 and his BSc degree in Geographic Information Systems from Zhejiang University (China) in 2008. Prior to joining the EPICentre, he worked as a software engineer on digital arts at TeamLab Inc (Japan). His research interests focus on Computer Graphics, Computer Vision, and Geographic Information Systems.
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